Development and Application of a Wax Deposition Prediction Model for the BoZi Crude Oil Pipeline

The BoZi pipeline transports high-wax-content crude oil, yet the spatial and temporal distribution of wax deposition thickness within the pipeline remains inadequately characterized. This study establishes a robust modeling framework that synergistically integrates field operational data with theoretical calculations to simulate and forecast the pipeline's production dynamics over a one-year period. The model, comprising 365 consecutive time points, accurately captures key operational phases including wax deposition growth, pigging operations, and pipeline shutdowns and restarts. Its predictive accuracy was rigorously validated against four critical operational parameters: inlet pressure, outlet temperature, volume of removed wax, and pigging pressure. Results demonstrate a high degree of model fidelity, with deviations of less than 1.01% for pressure, 1.9% for outlet temperature, 2% for pigging pressure, and 10.5% for the volume of wax removed. Leveraging this validated model, 11 pigging operations and shutdown-restart cycles were successfully executed on the BoZi pipeline. Furthermore, the model was employed to project long-term wax deposition trends and optimize pigging frequencies for the next 15 years.